Python List Comprehension

List comprehension is Python’s syntax to concisely create lists. The general syntax allows creating a new list by evaluating an expression on each element within the iterable and possibly further restricting the elements based on certain conditions.

Basic Syntax:

new_list = [expression for item in iterable if condition]

expression: the transformation or operation to be carried out on each element.
item: The iteration variable representing each element in the iterable.
iterable: the sequence of items being processed; e.g., a list, range, etc.
Condition: An optional filter that specifies whether an item should be included.

Examples:

1. Create a List from a Range:

squares = [x**2 for x in range(10)]
print(squares) # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Explanation: For each x in the range from 0 to 9, calculate x**2 and add it to the list.

2. Filter Items:

even_numbers = [x for x in range(10) if x % 2 == 0]
print(even_numbers) # Output: [0, 2, 4, 6, 8]

Explanation: Include only numbers that are divisible by 2.

3. Transform and Filter:

odd_squares = [x**2 for x in range(10) if x % 2 != 0]
print(odd_squares) # Output: [1, 9, 25, 49, 81]

Explanation: Square only the odd numbers in the range.

4. Nested Loops:

pairs = [(x, y) for x in range(3) for y in range(3)]
print(pairs) # Output: [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2), (2, 0), (2, 1), (2, 2)]

Explanation: Combine every x in range(3) with every y in range(3).

5. Flatten a Nested List:

matrix = [[1, 2], [3, 4], [5, 6]]
flattened = [num for row in matrix for num in row]
print(flattened) # Output: [1, 2, 3, 4, 5, 6]

Explanation: Loop through each row in the matrix, then loop through each num in the row.

6. List Comprehension with Functions:

def square(x):
    return x**2

results = [square(x) for x in range(5)]
print(results)  # Output: [0, 1, 4, 9, 16]

Explanation: Apply the square function to each number in the range.

Comparison with a For Loop:

Using list comprehension is often more concise than a traditional for loop.

Using a For Loop:

squares = []
for x in range(10):
    squares.append(x**2)
print(squares)  # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Using List Comprehension:

squares = [x**2 for x in range(10)]
print(squares) # Output: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

Advanced Usage:

1. Conditional Expression in `expression`:

results = ["Even" if x % 2 == 0 else "Odd" for x in range(5)]
print(results)  # Output: ['Even', 'Odd', 'Even', 'Odd', 'Even']

Explanation: Include conditional logic directly in the expression.

2. Set and Dictionary Comprehension:

Set Comprehension:

unique_squares = {x**2 for x in range(5)}
print(unique_squares) # Output: {0, 1, 4, 9, 16}

Dictionary Comprehension:

square_dict = {x: x**2 for x in range(5)}
print(square_dict) # Output: {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

Advantages of List Comprehension:

Conciseness: The code is shorter and more readable.
Performance: Usually, it runs faster than the equivalent for loops, due to Python’s specific implementation.
Readability: Clear intent to generate or transform lists.

When Not to Use:

If the logic is too complex, a traditional loop with comments might be more readable.
Avoid using list comprehensions for side effects (such as printing or modifying external variables).